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Review
. 2021 Apr;17(1):5-11.
doi: 10.17925/EE.2021.17.1.5. Epub 2021 Apr 28.

The DFUC 2020 Dataset: Analysis Towards Diabetic Foot Ulcer Detection

Bill Cassidy  1 , Neil D Reeves  2 , Joseph M Pappachan  2   3   4 , David Gillespie  1 , Claire O'Shea  5 , Satyan Rajbhandari  3 , Arun G Maiya  6 , Eibe Frank  7 , Andrew Jm Boulton  4 , David G Armstrong  8 , Bijan Najafi  9 , Justina Wu  5 , Rupinder Singh Kochhar  3 , Moi Hoon Yap  1
Affiliations
Review

The DFUC 2020 Dataset: Analysis Towards Diabetic Foot Ulcer Detection

Bill Cassidy et al. touchREV Endocrinol. 2021 Apr.

Abstract

Every 20 seconds a limb is amputated somewhere in the world due to diabetes. This is a global health problem that requires a global solution. The International Conference on Medical Image Computing and Computer Assisted Intervention challenge, which concerns the automated detection of diabetic foot ulcers (DFUs) using machine learning techniques, will accelerate the development of innovative healthcare technology to address this unmet medical need. In an effort to improve patient care and reduce the strain on healthcare systems, recent research has focused on the creation of cloud-based detection algorithms. These can be consumed as a service by a mobile app that patients (or a carer, partner or family member) could use themselves at home to monitor their condition and to detect the appearance of a DFU. Collaborative work between Manchester Metropolitan University, Lancashire Teaching Hospitals and the Manchester University NHS Foundation Trust has created a repository of 4,000 DFU images for the purpose of supporting research toward more advanced methods of DFU detection. This paper presents a dataset description and analysis, assessment methods, benchmark algorithms and initial evaluation results. It facilitates the challenge by providing useful insights into state-of-the-art and ongoing research.

Keywords: DFU dataset; Diabetic foot; deep learning; machine learning.

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Conflict of interest statement

Disclosure: Bill Cassidy, Neil D Reeves, Joseph M Pappachan, David Gillespie, Claire O’Shea, Satyan Rajbhandari, Arun G Maiya, Eibe Frank, Andrew JM Boulton, David G Armstrong, Bijan Najafi, Justina Wu, Rupinder Singh Kochhar and Moi Hoon Yap have no financial or non-financial relationships or activities to declare in relation to this article.

Figures

Figure 1:
Figure 1:. Experts’ annotation of the region of interest and the pathology label
Figure 2:
Figure 2:. Illustration of the image resizing methods
Figure 3:
Figure 3:. The ratio of annotated diabetic foot ulcers to foot images in the training set
Figure 4:
Figure 4:. Illustration of single detection results
Figure 5:
Figure 5:. Illustration of multiple detection results
Figure 6:
Figure 6:. Easy cases where wounds are visible and detected by all networks
Figure 7:
Figure 7:. Difficult cases where wounds are not easily visible and not detected by all networks
None
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See this image and copyright information in PMC

References

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